class_docstr: A preprocessing layer which randomly flips images during training.
  
  This layer will flip the images horizontally and or vertically based on the
  `mode` attribute. During inference time, the output will be identical to
  input. Call the layer with `training=True` to flip the input.
  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
  of integer or floating point dtype.
  By default, the layer will output floats.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
  
  Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
  
  Args:
      mode: String indicating which flip mode to use. Can be `"horizontal"`,
          `"vertical"`, or `"horizontal_and_vertical"`. `"horizontal"` is a
          left-right flip and `"vertical"` is a top-bottom flip. Defaults to
          `"horizontal_and_vertical"`
      seed: Integer. Used to create a random seed.
      **kwargs: Base layer keyword arguments, such as
          `name` and `dtype`.
code: def __init__(self, mode=HORIZONTAL_AND_VERTICAL, seed=None, **kwargs):
      super().__init__(**kwargs)
      self.seed = seed
      self.generator = SeedGenerator(seed)
      self.mode = mode
      self._convert_input_args = False
      self._allow_non_tensor_positional_args = True
  
file_path: keras/keras/layers/preprocessing/random_flip.py
imports: from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.random.seed_generator import SeedGenerator
label_desc: Use image node_id 1 to create a new RandomFlip object from inherited base classes: TFDataLayer with example: obj = RandomFlip(mode, seed)